Mercedes-Benz Korea's AI Agents

Mercedes-Benz Korea is pioneering a new approach to enterprise data analysis with the integration of AI agents, aiming to unlock trusted insights at scale. This initiative leverages the Databricks Data Intelligence Platform to create a unified semantic foundation for both traditional Business Intelligence (BI) and advanced AI applications.

The core challenge was ensuring AI agents could access and interpret business logic consistently. Mercedes-Benz Korea addressed this by extending its existing Lakehouse architecture and Power BI stack to create an open, AI-ready semantic layer. This layer makes over 500 Key Performance Indicator (KPI) definitions accessible via Databricks Unity Catalog Metric Views.

Unified Semantics for BI and AI

Previously, business semantics were split between Power BI's DAX language and curated tables in the Lakehouse. The new architecture consolidates this logic within Unity Catalog. This ensures that questions like "What's our total retail sales MTD by vehicle class?" yield consistent answers whether queried through BI reports or AI experiences.

This evolution moves beyond traditional reporting, extending governance to persona-based AI agents. Roles like CFO or Sales VP can now interact with tailored AI experiences that are still grounded in the same validated business logic.

Accelerating AI Readiness

To bridge the gap between existing Power BI metrics and the new semantic layer, Databricks developed an automated DAX-to-Metric-View transpiler. This tool parses Power BI semantic models, extracts DAX measures, and generates draft metric view definitions. It flags complex measures for manual review, saving hundreds of hours of manual migration work.

This automation significantly accelerates the creation of AI-ready semantics, allowing for faster deployment of AI capabilities.

Building Trusted AI Agents

The goal is a 100% match between AI-generated answers and existing Power BI reports. Mercedes-Benz Korea and Databricks have jointly documented best practices for curating and optimizing metric views and AI agent interactions. This focus on validation ensures the reliability and explainability of insights derived from the AI agents.

This initiative represents a significant step towards enabling self-service analytics for sales, product, finance, and marketing teams across global markets, establishing a repeatable playbook for deploying persona-based AI agents on a shared KPI layer. The development of a unified semantic layer for AI and BI is key to this strategy.